Overview

Dataset statistics

Number of variables15
Number of observations11000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory120.0 B

Variable types

Numeric11
Categorical4

Alerts

age is highly overall correlated with duration_entity and 1 other fieldsHigh correlation
base is highly overall correlated with otherHigh correlation
duration_entity is highly overall correlated with age and 1 other fieldsHigh correlation
duration_overtime is highly overall correlated with overtime_payHigh correlation
duration_total is highly overall correlated with age and 1 other fieldsHigh correlation
other is highly overall correlated with baseHigh correlation
overtime_pay is highly overall correlated with duration_overtimeHigh correlation
sector is highly imbalanced (75.3%)Imbalance
id has unique valuesUnique
bonus has 4472 (40.7%) zerosZeros
overtime_pay has 7243 (65.8%) zerosZeros
other has 2855 (26.0%) zerosZeros
duration_overtime has 7243 (65.8%) zerosZeros

Reproduction

Analysis started2023-12-20 17:29:37.567798
Analysis finished2023-12-20 17:30:08.130105
Duration30.56 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIQUE 

Distinct11000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99813.83
Minimum8
Maximum199986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size86.1 KiB
2023-12-20T17:30:08.292193image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile9365.8
Q149618.25
median99881.5
Q3149880.25
95-th percentile189908
Maximum199986
Range199978
Interquartile range (IQR)100262

Descriptive statistics

Standard deviation58047.975
Coefficient of variation (CV)0.58156244
Kurtosis-1.2032327
Mean99813.83
Median Absolute Deviation (MAD)50128
Skewness-0.0069536607
Sum1.0979521 × 109
Variance3.3695674 × 109
MonotonicityNot monotonic
2023-12-20T17:30:08.589035image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192064 1
 
< 0.1%
150038 1
 
< 0.1%
172121 1
 
< 0.1%
140632 1
 
< 0.1%
61821 1
 
< 0.1%
122546 1
 
< 0.1%
185937 1
 
< 0.1%
157064 1
 
< 0.1%
188591 1
 
< 0.1%
101308 1
 
< 0.1%
Other values (10990) 10990
99.9%
ValueCountFrequency (%)
8 1
< 0.1%
13 1
< 0.1%
57 1
< 0.1%
68 1
< 0.1%
92 1
< 0.1%
129 1
< 0.1%
176 1
< 0.1%
191 1
< 0.1%
196 1
< 0.1%
197 1
< 0.1%
ValueCountFrequency (%)
199986 1
< 0.1%
199980 1
< 0.1%
199970 1
< 0.1%
199903 1
< 0.1%
199898 1
< 0.1%
199887 1
< 0.1%
199874 1
< 0.1%
199839 1
< 0.1%
199825 1
< 0.1%
199819 1
< 0.1%

base
Real number (ℝ)

HIGH CORRELATION 

Distinct10677
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33376.738
Minimum10
Maximum241624.39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size86.1 KiB
2023-12-20T17:30:08.884642image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile8157.824
Q120995.115
median31341.245
Q341348.29
95-th percentile65389.224
Maximum241624.39
Range241614.39
Interquartile range (IQR)20353.175

Descriptive statistics

Standard deviation19276.552
Coefficient of variation (CV)0.5775445
Kurtosis9.9652998
Mean33376.738
Median Absolute Deviation (MAD)10164.915
Skewness2.1157075
Sum3.6714412 × 108
Variance3.7158544 × 108
MonotonicityNot monotonic
2023-12-20T17:30:09.173420image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24000 16
 
0.1%
15804 15
 
0.1%
25200 10
 
0.1%
15840 10
 
0.1%
25920 8
 
0.1%
18000 7
 
0.1%
36000 7
 
0.1%
19200 7
 
0.1%
26400 7
 
0.1%
8156 7
 
0.1%
Other values (10667) 10906
99.1%
ValueCountFrequency (%)
10 1
< 0.1%
334.2 1
< 0.1%
365.31 1
< 0.1%
401.78 1
< 0.1%
461 1
< 0.1%
495 1
< 0.1%
530.04 1
< 0.1%
551.8 1
< 0.1%
587.27 1
< 0.1%
622 1
< 0.1%
ValueCountFrequency (%)
241624.39 1
< 0.1%
228400 1
< 0.1%
216315.6 1
< 0.1%
206844.79 1
< 0.1%
184170.18 1
< 0.1%
182938.66 1
< 0.1%
165359.51 1
< 0.1%
165181 1
< 0.1%
164487.66 1
< 0.1%
159255.2 1
< 0.1%

bonus
Real number (ℝ)

ZEROS 

Distinct3833
Distinct (%)34.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2128.4862
Minimum0
Maximum258061
Zeros4472
Zeros (%)40.7%
Negative0
Negative (%)0.0%
Memory size86.1 KiB
2023-12-20T17:30:09.461848image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median620
Q32940.7175
95-th percentile7970.1
Maximum258061
Range258061
Interquartile range (IQR)2940.7175

Descriptive statistics

Standard deviation4966.4447
Coefficient of variation (CV)2.3333225
Kurtosis710.16259
Mean2128.4862
Median Absolute Deviation (MAD)620
Skewness17.95357
Sum23413348
Variance24665573
MonotonicityNot monotonic
2023-12-20T17:30:09.741758image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4472
40.7%
500 143
 
1.3%
1000 124
 
1.1%
400 98
 
0.9%
800 92
 
0.8%
700 83
 
0.8%
1500 79
 
0.7%
300 79
 
0.7%
600 74
 
0.7%
2000 69
 
0.6%
Other values (3823) 5687
51.7%
ValueCountFrequency (%)
0 4472
40.7%
5 1
 
< 0.1%
12 2
 
< 0.1%
20.1 1
 
< 0.1%
40 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
56 1
 
< 0.1%
60 1
 
< 0.1%
63 1
 
< 0.1%
ValueCountFrequency (%)
258061 1
< 0.1%
92622 1
< 0.1%
90828.25 1
< 0.1%
90629 1
< 0.1%
82270.5 1
< 0.1%
78926.5 1
< 0.1%
78277.92 1
< 0.1%
72219 1
< 0.1%
68018.95 1
< 0.1%
65460 1
< 0.1%

overtime_pay
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3629
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1679.2739
Minimum0
Maximum228110.34
Zeros7243
Zeros (%)65.8%
Negative0
Negative (%)0.0%
Memory size86.1 KiB
2023-12-20T17:30:10.005212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31139.33
95-th percentile8660.528
Maximum228110.34
Range228110.34
Interquartile range (IQR)1139.33

Descriptive statistics

Standard deviation5407.9643
Coefficient of variation (CV)3.2204182
Kurtosis440.13225
Mean1679.2739
Median Absolute Deviation (MAD)0
Skewness15.157801
Sum18472013
Variance29246078
MonotonicityNot monotonic
2023-12-20T17:30:10.288589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7243
65.8%
27.99 6
 
0.1%
597.12 5
 
< 0.1%
29.69 5
 
< 0.1%
74.64 4
 
< 0.1%
69.76 4
 
< 0.1%
149.28 4
 
< 0.1%
29.7 3
 
< 0.1%
732.48 3
 
< 0.1%
335.88 3
 
< 0.1%
Other values (3619) 3720
33.8%
ValueCountFrequency (%)
0 7243
65.8%
12.4 1
 
< 0.1%
18.88 1
 
< 0.1%
18.9 1
 
< 0.1%
19.64 1
 
< 0.1%
21.04 1
 
< 0.1%
21.19 1
 
< 0.1%
22.16 1
 
< 0.1%
22.39 1
 
< 0.1%
22.43 1
 
< 0.1%
ValueCountFrequency (%)
228110.34 1
< 0.1%
163366.98 1
< 0.1%
141859.9 1
< 0.1%
108148.6 1
< 0.1%
90438.22 1
< 0.1%
72589.37 1
< 0.1%
71315.07 1
< 0.1%
69353.5 1
< 0.1%
69155.2 1
< 0.1%
64959.87 1
< 0.1%

other
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8033
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2477.6258
Minimum0
Maximum88555.76
Zeros2855
Zeros (%)26.0%
Negative0
Negative (%)0.0%
Memory size86.1 KiB
2023-12-20T17:30:10.582028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2141.82
Q33497.43
95-th percentile5500.342
Maximum88555.76
Range88555.76
Interquartile range (IQR)3497.43

Descriptive statistics

Standard deviation3715.4191
Coefficient of variation (CV)1.4995885
Kurtosis119.62093
Mean2477.6258
Median Absolute Deviation (MAD)1541.255
Skewness8.821457
Sum27253884
Variance13804339
MonotonicityNot monotonic
2023-12-20T17:30:11.115318image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2855
 
26.0%
1346.4 5
 
< 0.1%
2448 4
 
< 0.1%
2142 4
 
< 0.1%
1570.8 3
 
< 0.1%
3304.8 3
 
< 0.1%
2958 3
 
< 0.1%
3060 3
 
< 0.1%
5000 3
 
< 0.1%
1880.68 3
 
< 0.1%
Other values (8023) 8114
73.8%
ValueCountFrequency (%)
0 2855
26.0%
28.4 1
 
< 0.1%
39 1
 
< 0.1%
45.05 1
 
< 0.1%
46.9 1
 
< 0.1%
49.9 1
 
< 0.1%
53.03 1
 
< 0.1%
59.61 1
 
< 0.1%
59.76 1
 
< 0.1%
61.57 1
 
< 0.1%
ValueCountFrequency (%)
88555.76 1
< 0.1%
71063.33 1
< 0.1%
69816.11 1
< 0.1%
64019.36 1
< 0.1%
62959.5 1
< 0.1%
61085.66 1
< 0.1%
59176.9 1
< 0.1%
57979.5 1
< 0.1%
55185.4 1
< 0.1%
52921.46 1
< 0.1%

sector
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.1 KiB
1
10548 
2
 
452

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 10548
95.9%
2 452
 
4.1%

Length

2023-12-20T17:30:12.143367image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-20T17:30:12.436006image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 10548
95.9%
2 452
 
4.1%

Most occurring characters

ValueCountFrequency (%)
1 10548
95.9%
2 452
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10548
95.9%
2 452
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common 11000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10548
95.9%
2 452
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10548
95.9%
2 452
 
4.1%

section_07
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.1 KiB
2
5867 
3
2732 
1
2401 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 5867
53.3%
3 2732
24.8%
1 2401
21.8%

Length

2023-12-20T17:30:12.813160image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-20T17:30:13.843462image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2 5867
53.3%
3 2732
24.8%
1 2401
21.8%

Most occurring characters

ValueCountFrequency (%)
2 5867
53.3%
3 2732
24.8%
1 2401
21.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 5867
53.3%
3 2732
24.8%
1 2401
21.8%

Most occurring scripts

ValueCountFrequency (%)
Common 11000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 5867
53.3%
3 2732
24.8%
1 2401
21.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 5867
53.3%
3 2732
24.8%
1 2401
21.8%

sex
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.1 KiB
2
8289 
1
2711 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 8289
75.4%
1 2711
 
24.6%

Length

2023-12-20T17:30:14.406320image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-20T17:30:15.057675image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2 8289
75.4%
1 2711
 
24.6%

Most occurring characters

ValueCountFrequency (%)
2 8289
75.4%
1 2711
 
24.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 8289
75.4%
1 2711
 
24.6%

Most occurring scripts

ValueCountFrequency (%)
Common 11000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 8289
75.4%
1 2711
 
24.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 8289
75.4%
1 2711
 
24.6%

education
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7641818
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size86.1 KiB
2023-12-20T17:30:15.280794image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2307353
Coefficient of variation (CV)0.44524396
Kurtosis-0.065291546
Mean2.7641818
Median Absolute Deviation (MAD)0
Skewness1.0255588
Sum30406
Variance1.5147093
MonotonicityNot monotonic
2023-12-20T17:30:15.555621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 6633
60.3%
4 1983
 
18.0%
5 906
 
8.2%
3 680
 
6.2%
1 430
 
3.9%
6 368
 
3.3%
ValueCountFrequency (%)
1 430
 
3.9%
2 6633
60.3%
3 680
 
6.2%
4 1983
 
18.0%
5 906
 
8.2%
6 368
 
3.3%
ValueCountFrequency (%)
6 368
 
3.3%
5 906
 
8.2%
4 1983
 
18.0%
3 680
 
6.2%
2 6633
60.3%
1 430
 
3.9%

contract
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size86.1 KiB
1
9306 
2
1694 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9306
84.6%
2 1694
 
15.4%

Length

2023-12-20T17:30:15.790487image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-20T17:30:15.964024image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9306
84.6%
2 1694
 
15.4%

Most occurring characters

ValueCountFrequency (%)
1 9306
84.6%
2 1694
 
15.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9306
84.6%
2 1694
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
Common 11000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9306
84.6%
2 1694
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9306
84.6%
2 1694
 
15.4%

age
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.470182
Minimum19
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size86.1 KiB
2023-12-20T17:30:16.171344image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile26
Q134
median43
Q351
95-th percentile57
Maximum77
Range58
Interquartile range (IQR)17

Descriptive statistics

Standard deviation10.01214
Coefficient of variation (CV)0.23574517
Kurtosis-0.91071881
Mean42.470182
Median Absolute Deviation (MAD)8
Skewness-0.077343285
Sum467172
Variance100.24295
MonotonicityNot monotonic
2023-12-20T17:30:16.444529image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53 407
 
3.7%
50 387
 
3.5%
51 385
 
3.5%
47 379
 
3.4%
43 368
 
3.3%
48 366
 
3.3%
49 364
 
3.3%
52 363
 
3.3%
44 358
 
3.3%
55 343
 
3.1%
Other values (43) 7280
66.2%
ValueCountFrequency (%)
19 2
 
< 0.1%
20 7
 
0.1%
21 17
 
0.2%
22 42
 
0.4%
23 72
 
0.7%
24 120
1.1%
25 162
1.5%
26 218
2.0%
27 260
2.4%
28 235
2.1%
ValueCountFrequency (%)
77 2
 
< 0.1%
71 1
 
< 0.1%
69 2
 
< 0.1%
68 1
 
< 0.1%
67 7
 
0.1%
66 3
 
< 0.1%
65 14
 
0.1%
64 43
0.4%
63 45
0.4%
62 35
0.3%

duration_total
Real number (ℝ)

HIGH CORRELATION 

Distinct537
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.56708
Minimum0.01
Maximum57.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size86.1 KiB
2023-12-20T17:30:16.727569image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile2
Q19.03
median19.035
Q327.11
95-th percentile36.01
Maximum57.02
Range57.01
Interquartile range (IQR)18.08

Descriptive statistics

Standard deviation10.99695
Coefficient of variation (CV)0.59228214
Kurtosis-1.0388812
Mean18.56708
Median Absolute Deviation (MAD)9.015
Skewness0.04294215
Sum204237.88
Variance120.93291
MonotonicityNot monotonic
2023-12-20T17:30:16.982641image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.02 89
 
0.8%
0.02 79
 
0.7%
29.02 76
 
0.7%
2.02 73
 
0.7%
23.02 72
 
0.7%
26.02 71
 
0.6%
1.02 70
 
0.6%
27.02 70
 
0.6%
20.02 65
 
0.6%
8.02 63
 
0.6%
Other values (527) 10272
93.4%
ValueCountFrequency (%)
0.01 34
0.3%
0.02 79
0.7%
0.03 13
 
0.1%
0.04 24
 
0.2%
0.05 7
 
0.1%
0.06 13
 
0.1%
0.07 16
 
0.1%
0.08 11
 
0.1%
0.09 14
 
0.1%
0.1 14
 
0.1%
ValueCountFrequency (%)
57.02 1
< 0.1%
51.03 1
< 0.1%
47.05 1
< 0.1%
46.1 1
< 0.1%
46.06 1
< 0.1%
46.02 1
< 0.1%
46.01 1
< 0.1%
46 1
< 0.1%
45 2
< 0.1%
44.08 1
< 0.1%

duration_entity
Real number (ℝ)

HIGH CORRELATION 

Distinct487
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.401382
Minimum0.01
Maximum46.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size86.1 KiB
2023-12-20T17:30:17.245557image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.08
Q13.07
median9.1
Q318.04
95-th percentile29.02
Maximum46.01
Range46
Interquartile range (IQR)14.97

Descriptive statistics

Standard deviation9.3221809
Coefficient of variation (CV)0.81763606
Kurtosis-0.28526995
Mean11.401382
Median Absolute Deviation (MAD)6.93
Skewness0.74928019
Sum125415.2
Variance86.903056
MonotonicityNot monotonic
2023-12-20T17:30:17.507289image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 213
 
1.9%
2.02 167
 
1.5%
3.02 153
 
1.4%
1.02 145
 
1.3%
11.02 125
 
1.1%
4.02 122
 
1.1%
10.02 109
 
1.0%
1 108
 
1.0%
8.02 105
 
1.0%
7.02 100
 
0.9%
Other values (477) 9653
87.8%
ValueCountFrequency (%)
0.01 82
 
0.7%
0.02 213
1.9%
0.03 30
 
0.3%
0.04 84
 
0.8%
0.05 27
 
0.2%
0.06 42
 
0.4%
0.07 47
 
0.4%
0.08 29
 
0.3%
0.09 37
 
0.3%
0.1 52
 
0.5%
ValueCountFrequency (%)
46.01 1
< 0.1%
44.05 1
< 0.1%
44.03 1
< 0.1%
44.01 1
< 0.1%
43.02 2
< 0.1%
42.09 1
< 0.1%
42.07 1
< 0.1%
42.06 1
< 0.1%
42.03 1
< 0.1%
42.01 1
< 0.1%

duration_nominal
Real number (ℝ)

Distinct2926
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1301.3074
Minimum12.8
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size86.1 KiB
2023-12-20T17:30:17.771536image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum12.8
5-th percentile306.57
Q1766.8
median1591.9
Q31768
95-th percentile1840
Maximum2024
Range2011.2
Interquartile range (IQR)1001.2

Descriptive statistics

Standard deviation540.18923
Coefficient of variation (CV)0.41511269
Kurtosis-1.1106581
Mean1301.3074
Median Absolute Deviation (MAD)232.1
Skewness-0.6058206
Sum14314382
Variance291804.41
MonotonicityNot monotonic
2023-12-20T17:30:18.057562image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
766.8 654
 
5.9%
1816 635
 
5.8%
1800 228
 
2.1%
1808 211
 
1.9%
1776 192
 
1.7%
1824 186
 
1.7%
1792 182
 
1.7%
1784 176
 
1.6%
1760 143
 
1.3%
1832 140
 
1.3%
Other values (2916) 8253
75.0%
ValueCountFrequency (%)
12.8 1
< 0.1%
13 1
< 0.1%
15.3 2
< 0.1%
16.28 1
< 0.1%
16.63 1
< 0.1%
18.36 1
< 0.1%
18.55 1
< 0.1%
19.65 1
< 0.1%
23.95 1
< 0.1%
24.3 1
< 0.1%
ValueCountFrequency (%)
2024 4
< 0.1%
2016 1
 
< 0.1%
2008 4
< 0.1%
1992 4
< 0.1%
1984 4
< 0.1%
1976 2
< 0.1%
1975 1
 
< 0.1%
1968 3
< 0.1%
1960 4
< 0.1%
1952 1
 
< 0.1%

duration_overtime
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1240
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.702325
Minimum0
Maximum1812.58
Zeros7243
Zeros (%)65.8%
Negative0
Negative (%)0.0%
Memory size86.1 KiB
2023-12-20T17:30:18.331145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q340
95-th percentile260
Maximum1812.58
Range1812.58
Interquartile range (IQR)40

Descriptive statistics

Standard deviation111.96387
Coefficient of variation (CV)2.3471365
Kurtosis38.711103
Mean47.702325
Median Absolute Deviation (MAD)0
Skewness4.8052824
Sum524725.57
Variance12535.908
MonotonicityNot monotonic
2023-12-20T17:30:18.603163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7243
65.8%
12 55
 
0.5%
1 52
 
0.5%
2 37
 
0.3%
15 34
 
0.3%
5 34
 
0.3%
4 33
 
0.3%
48 29
 
0.3%
60 29
 
0.3%
8 29
 
0.3%
Other values (1230) 3425
31.1%
ValueCountFrequency (%)
0 7243
65.8%
0.5 1
 
< 0.1%
1 52
 
0.5%
1.1 1
 
< 0.1%
1.26 1
 
< 0.1%
1.3 1
 
< 0.1%
1.4 1
 
< 0.1%
1.5 4
 
< 0.1%
1.56 1
 
< 0.1%
1.8 1
 
< 0.1%
ValueCountFrequency (%)
1812.58 1
< 0.1%
1506.75 1
< 0.1%
1487 1
< 0.1%
1438.4 1
< 0.1%
1418 1
< 0.1%
1412 1
< 0.1%
1370.93 1
< 0.1%
1258.28 1
< 0.1%
1246.68 1
< 0.1%
1227.58 1
< 0.1%

Interactions

2023-12-20T17:30:05.065395image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:38.560271image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:41.244007image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:44.630133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:47.683944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:49.819765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:51.989214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:54.140925image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:56.968209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:00.636940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:02.765588image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:05.293247image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:38.773491image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:41.586407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:44.954202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:47.915791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:50.031146image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:52.195178image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:54.349547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:57.222513image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:00.841835image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:02.987018image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:05.491677image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:38.990029image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:41.910558image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:45.186441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:48.097388image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:50.226476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:52.380432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:54.543194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:57.908745image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:01.036269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:03.214832image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:05.700094image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:39.182167image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:42.237435image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:45.376508image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:48.289149image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:50.423290image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:52.588519image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:54.749744image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:58.219532image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:01.217143image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:03.419257image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:05.902511image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:39.370293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:42.476972image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:46.314203image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:48.486803image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:50.618399image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:52.788538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:54.927075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:58.516881image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:01.397946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:03.620220image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:06.096601image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:39.558585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:42.789487image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:46.490377image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:48.662466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:50.801284image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:52.965420image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:55.116046image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:58.849276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:01.579350image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:03.813952image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:06.309150image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:39.753572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:43.112767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:46.677951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:48.841850image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:50.982656image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:53.150510image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:55.418394image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:59.131954image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:01.765956image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:04.011213image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:06.510120image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:40.057765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:43.436998image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:46.866648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:49.024730image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:51.161547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:53.342616image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:55.693834image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:59.456943image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:01.948703image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:04.226133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:06.712133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:40.294871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:43.760799image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:47.065333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:49.223625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:51.349215image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:53.532021image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:55.938106image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:59.755529image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:02.154021image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:04.428103image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:06.906909image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:40.533211image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:44.059579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:47.247653image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:49.403651image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:51.546256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:53.732099image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:56.260432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:00.061805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:02.348686image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:04.635427image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:07.126001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:40.878017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:44.343009image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:47.488759image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:49.626675image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:51.782862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:53.938105image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:29:56.623223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:00.387545image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:02.564804image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-20T17:30:04.852223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2023-12-20T17:30:18.818339image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
agebasebonuscontractduration_entityduration_nominalduration_overtimeduration_totaleducationidotherovertime_paysection_07sectorsex
age1.0000.2130.0940.3300.5410.161-0.0580.8770.265-0.0090.134-0.0520.0780.0390.206
base0.2131.0000.1050.3190.3880.1640.2380.270-0.384-0.0100.5360.2520.1960.0560.152
bonus0.0940.1051.0000.0080.0850.413-0.1950.1330.1640.0010.094-0.1970.0410.0200.059
contract0.3300.3190.0081.000-0.461-0.210-0.071-0.344-0.1010.008-0.221-0.0730.1040.0880.046
duration_entity0.5410.3880.085-0.4611.0000.1510.1010.6580.133-0.0140.2190.1080.0800.1180.099
duration_nominal0.1610.1640.413-0.2100.1511.000-0.4340.2260.4040.001-0.062-0.4400.4190.1080.100
duration_overtime-0.0580.238-0.195-0.0710.101-0.4341.000-0.045-0.347-0.0210.3510.9980.1780.0590.062
duration_total0.8770.2700.133-0.3440.6580.226-0.0451.0000.311-0.0110.140-0.0410.0690.0570.150
education0.265-0.3840.164-0.1010.1330.404-0.3470.3111.0000.010-0.387-0.3590.2820.0880.194
id-0.009-0.0100.0010.008-0.0140.001-0.021-0.0110.0101.000-0.001-0.0210.0140.0120.000
other0.1340.5360.094-0.2210.219-0.0620.3510.140-0.387-0.0011.0000.3630.0940.0370.097
overtime_pay-0.0520.252-0.197-0.0730.108-0.4400.998-0.041-0.359-0.0210.3631.0000.0740.0000.048
section_070.0780.1960.0410.1040.0800.4190.1780.0690.2820.0140.0940.0741.0000.1850.102
sector0.0390.0560.0200.0880.1180.1080.0590.0570.0880.0120.0370.0000.1851.0000.016
sex0.2060.1520.0590.0460.0990.1000.0620.1500.1940.0000.0970.0480.1020.0161.000

Missing values

2023-12-20T17:30:07.461765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-20T17:30:07.896863image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idbasebonusovertime_payothersectorsection_07sexeducationcontractageduration_totalduration_entityduration_nominalduration_overtime
019206426651.530.00.000.00132414933.037.061524.150.0
12549540168.501500.00.003414.32121113610.076.011562.400.0
214216420134.800.00.001700.41122415228.0819.051816.000.0
319803416475.000.00.001305.00122515535.0711.011816.000.0
414499034797.600.01893.353118.73122215027.0019.01722.8063.0
52608348775.542500.07001.204741.02122214723.0123.01766.80206.0
617068942309.752390.00.003457.49112214612.063.021485.000.0
74201513668.880.00.001161.8512221314.071.02464.000.0
82719754326.462840.00.004335.78112213812.074.011584.000.0
95347229403.162670.00.002614.15122415411.0011.001616.000.0
idbasebonusovertime_payothersectorsection_07sexeducationcontractageduration_totalduration_entityduration_nominalduration_overtime
1099013941930955.12925.000.002611.32112213010.0310.031824.000.00
1099110472351640.29557.000.004389.42121116010.065.041562.060.00
1099211341242255.340.001411.563705.76122214424.0724.07651.6037.82
109935389643533.030.0013888.964862.24122214524.0224.02766.80387.00
109946713129497.900.0074.002513.6112211337.077.071562.401.00
109954159736573.221323.651276.803337.32122215027.0227.02766.8040.00
1099612002228280.005470.000.002385.13112414118.0815.091792.000.00
1099741800109316.960.000.009042.58112214720.021.071656.000.00
1099815384957721.356950.000.004906.32112214728.0121.101784.000.00
1099972437676.52700.00730.313296.65122215131.0117.02666.0023.00